Democracy, Income Inequality and the Resource Curse: Investigation of Causality

1 Introduction

It has been almost two decades since Ross’s classical work that introduced the resource curse into political science and made the political resource curse a mainstream (Ross 2001). Nevertheless, we still have much ambiguity and lacunas as to why resource endowment is so bad for institutional development and democratization.

Historically, resources were considered a blessing, the most important being fertile land and gold. In the 19 century, energy as an industry has substantially advanced and coal became a foundation of every economy. And later we have seen petroleum take its place as a basis for the energy industry, trade, and most of the goods. A transition to oil that was so eloquently described in the quoted book “Carbon democracy” (Mitchell 2011) had a critical effect on politics. Modern democracy could not be possible without technological advances in the field of energy. Thus, expectedly it has been considered a blessing for oil-rich countries for a long time. Not to mention, metals and mineral resources were considered to be a godsend. However, petroleum had adverse consequences as well. According to Ross, petroleum was not a predicament until the 1970s when the oil prices spiked. The resource curse is not traced until then. After that, oil dependence became an important factor of low economic growth, a lack of diversified economy, a higher chance of civil conflict, and failure of democratization. Alike, comparative studies of new states in Africa have made minerals bad reputation (Jensen and Wantchekon 2004; Ross 2013; Wantchekon 2002).

Nevertheless, initial thought on the hazardous consequences of resource abundance was later reconsidered. Many authors have stated that the resource curse is a more complicated phenomenon that is conditioned by the institutional environment (De Ferranti et al. 2002; Haber and Menaldo 2011; Wright and Czelusta 2004). They have declared that natural-resource wealth is not hazardous per se and, in fact, different resources are different in their political outcomes. While some claim that the resource curse theory is valid, but negative effects of it are alleviated by good institutions. The presence of institutions of high-quality can mitigate the rent-seeking, grabbing behavior of politicians, reduce corruption, which are the main reasons for the resource curse, according to the traditional theory. Stable democ-racies are observed to not experience the curse in most cases (Canada, Norway). Moreover, empirical studies have sometimes had contrasting inferences that depend on the research design (methods, operationalization, and empirical base). Some conclude that resources are an important determinant of democratic breakdown (Jensen and Wantchekon 2004; Ramsay 2011; Ross 2001), and others indicate that either authoritarianism leads to resource depen-dence (Gurses 2009; Haber 2008; Haber and Menaldo 2011) or there is no real connection (Claessens and Laeven 2003; Herb 2005; Wright and Czelusta 2004). Hence, the problem of this research is the absence of a strict consensus on the issue of the direction of the causal


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relationship between the political regime and resource dependence in political science.

To address the issue of the institutional environment and employ a social factor that may moderate the relationship and determine how the resource curse happens, I include in my theoretical mechanism the phenomenon of inequality. I presuppose that dominating inequality (in the thesis I focus on income inequality in particular) should affect how resource rents are redistributed among people and such redistribution affects the probability that petro-elites are able to concentrate political power and reverse democratic processes.1 In the thesis, I answer the following puzzle question: what is the type of the causal relationship between the political regime and resource dependence in the circumstances of high- and low-income inequality? The goal of my research is to uncover the direction and type of the relationship. I use the relationship between the political regime and resource dependence in countries of high- and low-income inequality as the subject of the work.

Considering the existing literature, I suggest that the curse does exist, but is dependent on the institutional environment. Economic relations expressed in the income inequality affect the relationship. Low-inequality-type countries are able to escape the resource curse via the redistribution of resource boons. I formulate several hypotheses that are going to be tested in the course of the research.

H1: The type of the relationship between the political regime and resource dependence varies across low- and high-inequality countries.

– H1.1: The effect of resource dependence is negative in high-inequality countries.

– H1.2: Resource dependence does not have a significant effect in low-inequality countries.

H2: The political regime does not influence resource dependence.

To illustrate the hypotheses I present causal graph 1. A more elaborate explanation is expressed in the following chapters.

Political Regime



Income Inequality



Resource Dependence




Resource Abundance

Figure 1: Causal Graph

I fulfill several tasks to succeed in the goal of the research.

1. For more details see Chapter 2 paragraph 2.5


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1. To propose an underlying theoretical explanation for the causal mechanism;

2. To prove that resource dependence is exogenous to the political regime, using the instrumental variable (IV) method;

3. To estimate the relationship between the political regime and resource dependence in countries with low and high inequality.

I exploit an extensive sample of 190 countries and 39 years. It has observations with contrasting resource dependence, regime, and inequality types. The data includes socio-economic characteristics as well as information about resource wealth.

This paper has a relatively novel approach by including inequality as a moderating com-ponent and trying to reveal the causal mechanism with statistical methods. I employ the instrumental variable method (2SLS) to address the confounding problem (presence of con-founder may cause spurious relationship between the outcome and covariate; as I am applying causal inference approach it is essential to eliminate it). The IV method was used before in the context of economic, but not the political resource curse. Furthermore, I moderate the connection with the income-inequality type. The moderator allows me to estimate the inter-action effect of two independent variables, which serves to test my hypotheses. The reason why the endogeneity problem remains such an issue is that most research uses observational data (Ross 2015) and experimental methods are cumbersome to use in this topic. That is why my design can enable science to progress in this direction.

In the first chapter, I analyze existing theoretical frameworks on the political resource curse, juxtapose them, and suggest own approach, which includes inequality as a third factor, and enables to work in terms of causal inference. Then I discuss the methods and data I exploit. I provide empirical analysis in the third chapter. Finally, I discuss the results and make conclusions.

Contents

1 Introduction 3
2 Chapter 1. Theory 6
2.1 Classical Political Resource Curse . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Reinvention of the Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 New Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.4 Inequality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.5 Theoretical Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3 Chapter 2. Data and Methodology 15
3.1 Dependent Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2 Independent Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.3 Control Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.4 Preliminary Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.5 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.5.1 Imputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.5.2 Instrumental Variable . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.5.3 Interaction variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4 Chapter 3. Results 20
4.1 General Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.2 Model with Interaction Variable . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.3 Model of First Difference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.4 Robustness Check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.4.1 Nonlinearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.4.2 Influential Observations . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.4.3 VDem-Polyarchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
5 Discussion and Conclusion 27
References 30
6 Appendix 1 36
7 Appendix 2 37
8 Appendix 3 39
9 Appendix 4 40
10 Appendix 5 40

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